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Dig Deeper - Microbial Community Analysis

Project Background

RestREco is a research initiative that approaches ecological restoration with a focus on resilience, rather than returning ecosystems to their original state.

The Dig Deeper study explored how the age of restoration, establishment type, and site management influence bacterial and fungal communities in soil. This was achieved by analysing both 16S and ITS sequencing data collected from 66 distinct sites. The analysis focused on three main aspects:

  • Alpha and beta diversity
  • Taxonomic composition
  • Functional diversity

Data Overview

Approach

The sequencing data were processed using the QIIME 2 bioinformatics platform — a widely used tool for microbiome analysis. Raw amplicon reads were denoised using the DADA2 plugin, enabling accurate identification of amplicon sequence variants (ASVs) with single-nucleotide resolution. This method improves upon traditional OTU clustering by enhancing precision.

After quality filtering and feature table construction, the pipeline proceeded to:

  • Generate taxonomic classifications
  • Perform alpha and beta diversity analyses
  • Create various interactive visualisations for exploring microbial community structure

Both 16S rRNA gene sequencing (for bacteria and archaea) and ITS sequencing (for fungi) were included, providing a broad overview of microbial diversity across samples.

Quality Control

You can explore the full MultiQC report by clicking the image below:

MutlitQc thumbnail

Figure 5. MultiQC Plot

This barplot presents the number of raw sequencing reads for each individual sample prior to any quality control or filtering.
Most samples exhibit a relatively consistent sequencing depth. However, a few samples display notably lower read counts, which could potentially influence downstream analyses if not properly filtered or normalised.

About the Sites

The dataset encompasses 66 distinct sites, each contributing five soil samples. These sites span a wide age range — from 1 year to over 100 years — providing a valuable gradient for ecological comparisons.

Each site’s five samples:

  • Share the same establishment type
  • Are subject to the same management practices

In addition, soil pH and electrical conductivity (EC) were measured for every sample to help characterise environmental conditions.

There are:

  • 3 establishment types

  • 4 management types, which can be applied individually or in combination

    → Some sites follow a single management approach, while others incorporate two, three, or all four.

Mean Soil pH Across Sites

Soil pH is a critical environmental parameter that influences microbial community structure, nutrient availability, and overall ecosystem function.
This section summarises the average pH values for each sampled site, grouped by establishment type.

The bar chart below allows for easy comparison of mean pH across sites.
Each bar is coloured according to the establishment method (e.g., seed mix, natural regeneration, green hay), and by hovering over a bar, the user can view the precise pH value for each site.

Note: To improve clarity, site names have been removed from the x-axis, but full details are available via the interactive tooltip.

Figure 2. pH Mean For Each Site

Mean Electrical Conductivity

This section explores the variation in electrical conductivity (EC) across study sites.
Electrical conductivity is a measure of the soil’s ability to conduct electricity, often reflecting ion concentration and soil salinity, which can influence microbial activity and nutrient availability.

The plots below allow users to examine how EC differs depending on either the type of establishment or the age of the site.

Use the drop-down menu to switch between views. Hover over the bars for detailed site-specific values.

This plot shows the variation in electrical conductivity across sites, sorted by electrical conductivity.

Site Management Practices

This section illustrates the types of management practices applied at each site, including cutting, cattle grazing, sheep grazing, and ploughing.
Each coloured bar indicates the presence of one or more management strategies at a given site. Sites with multiple bars have undergone combinations of practices, highlighting the complexity and variation in land use across the study area.

Hover over each bar in the interactive plot to see the site name.

Figure 4. Management type for each site

Influence of Management Practices

This section examines how different management practices — such as cutting, grazing by cattle or sheep, and ploughing — influence two key soil properties: pH and electrical conductivity (EC).
These soil characteristics can affect microbial communities by altering nutrient availability, pH balance, and soil structure.

The visualisations below display the overall effect of each management type individually. However, it is important to note that potential interactions between management types (e.g., cutting combined with grazing) are not accounted for here.
Such interactions may play a significant role in shaping soil conditions but were beyond the scope of this visual summary.

Use the drop-down menu to explore how each management type affects pH or EC across all sampled sites.
Black dots represent the mean values for each management category.

Diversity

Alpha Diversity

Alpha diversity refers to the variety of organisms within a particular sample or environment. It reflects both richness—the number of distinct taxa—and evenness—how evenly individuals are distributed among those taxa. One of the most widely used measures for assessing alpha diversity is the Shannon index.

The Shannon index takes into account not only the number of species present, but also how evenly their abundances are distributed. A higher Shannon value generally indicates a more diverse and ecologically balanced community.

Another important metric is Faith’s Phylogenetic Diversity (Faith PD), which measures the total branch length of the phylogenetic tree that spans the species in a sample. Unlike the Shannon index, Faith PD incorporates evolutionary relationships, providing a phylogenetic perspective on diversity.

In the interactive plots below, we examine how both the Shannon index and Faith PD vary across different environmental and experimental conditions, separately for the 16S (bacteria and archaea) and ITS (fungi) datasets.

From 16S Data

You can also directly go explore the plots on QIIME2:
Click here to explore the Shannon plots
Click here to explore the faith PD plots

From ITS Data

Just as with the 16S data, we computed the Shannon diversity index for the ITS dataset to assess fungal alpha diversity. The resulting boxplots allow us to explore how fungal diversity varies across different environmental or experimental conditions, offering a parallel view to that of bacterial and archaeal communities.

This analysis provides valuable insights into how fungal communities respond to factors such as establishment method, site age, or land management, complementing the microbial diversity picture captured by the 16S data.

Beta Diversity

To explore differences in microbial communities, we often rely on dimensionality reduction techniques such as Principal Coordinates Analysis (PCoA), visualised through Emperor plots. Two commonly used distance metrics in this context are Bray-Curtis and Weighted UniFrac.

While both metrics can reveal meaningful clustering and separation in microbial data, they capture complementary aspects of community structure.

From 16S Data

Bray-Curtis Emperor Plot

The Bray-Curtis Emperor plot is a 3D visualisation of microbial community dissimilarities between samples, based on the Bray-Curtis distance. This distance metric quantifies how different two samples are in terms of species abundance, taking into account both presence/absence and relative abundances. It does not incorporate evolutionary relationships between features.

Using Principal Coordinates Analysis (PCoA), the high-dimensional Bray-Curtis distance matrix is projected into a lower-dimensional space—typically three axes—to capture the main patterns of variation across samples.

The Emperor plot is an interactive 3D tool developed for QIIME 2 that allows users to explore these PCoA results. Samples are represented as points in space, and their spatial proximity reflects ecological similarity:

  • Samples that are closer together have more similar microbial communities.
  • Samples that are further apart differ more strongly in community composition.

This type of plot is particularly useful for identifying clustering by experimental groups—such as treatment, site, or timepoint—and for detecting patterns or gradients in microbial diversity.

Here is a link to the bray curtis emperor plot for more flexibility on QIIME2: Bray-Curtis Emperor Plot (16S)

Weighted UniFrac Emperor Plot

In contrast, Weighted UniFrac incorporates both species abundance and phylogenetic relationships. It measures the dissimilarity between microbial communities by accounting for how much evolutionary history is shared between them, weighted by the relative abundance of taxa.

This makes Weighted UniFrac particularly useful when the evolutionary context is important, as it highlights not only which organisms are present and in what quantities, but also how closely related they are.

As with Bray-Curtis, Principal Coordinates Analysis (PCoA) is applied to the distance matrix, and the results are displayed using an interactive Emperor plot. This allows for intuitive exploration of patterns in microbial composition, helping to reveal whether certain groups cluster together based on phylogenetic similarity and experimental conditions.

Here is a link to the weighted unifrac emperor plot for more flexibility on QIIME2: Weighted Unifrac Emperor Plot (16S)

From ITS Data

Taxonomy Composition

Krona Plots

To explore the composition of soil microbial communities, we used Krona plots — interactive, circular charts that display taxonomic abundances in a hierarchical manner.

These plots allow users to intuitively navigate from broader taxonomic levels (such as Phylum) to more specific ones (like Genus), while simultaneously comparing relative abundances across taxa.

In this study, Krona plots provide a powerful and user-friendly way to:

  • Visualise which microbial groups dominate each site
  • Explore the taxonomic diversity present in bacterial, archaeal, and fungal communities

You can click on the image below to access the Krona plots for each site.

Krona thumbnail

Figure 2. Krona Plot for Baltic_farm_1

Functional Diversity

In microbial ecology, a guild refers to a group of organisms that fulfil similar ecological roles, regardless of their taxonomic identity. Understanding functional guilds allows researchers to move beyond taxonomic profiles and assess the ecological roles that microbial communities may play in an environment.

From 16S Data

From ITS Data

To investigate the ecological roles of fungal communities, we used FUNGuild, a tool that assigns fungi to functional guilds based on curated databases and literature. These guilds represent ecological strategies such as:

  • Saprotrophs: decomposers of organic matter
  • Mycorrhizal fungi: symbionts associated with plant roots
  • Pathogens: organisms that cause disease in plants or animals
  • Ectomycorrhizal: symbionts that assist plant roots by enhancing nutrient and water uptake, especially nitrogen and phosphorus, while contributing to carbon cycling and soil nutrient mobilisation
  • Arbuscular Mycorrhizal: symbionts that contribute to nutrient uptake
  • Endophyte: microorganisms that promote the growth and development of the plants
  • Lichenized: fungi that form symbiotic associations with algae or cyanobacteria, creating lichens that can survive in harsh environments by combining structural support and photosynthetic ability
  • Parasites: fungi that live on or inside a host organism, extracting nutrients and often causing harm
  • Symbiotrophs: fungi that engage in mutually beneficial relationships with host organisms, typically exchanging nutrients for resources like carbon or protection

This functional classification provides valuable insights into what fungi are likely doing in the ecosystem, beyond simply who they are.

The plot below highlights the top 20 most abundant fungal guilds identified using FUNGuild. To avoid clutter, the guild names are hidden on the axis; however, users can hover over each bar to reveal the full name, enabling interactive and detailed exploration of fungal functional diversity.